Literature DB >> 27935480

Optimal compensation for neuron loss.

David Gt Barrett1,2, Sophie Denève1, Christian K Machens2.   

Abstract

The brain has an impressive ability to withstand neural damage. Diseases that kill neurons can go unnoticed for years, and incomplete brain lesions or silencing of neurons often fail to produce any behavioral effect. How does the brain compensate for such damage, and what are the limits of this compensation? We propose that neural circuits instantly compensate for neuron loss, thereby preserving their function as much as possible. We show that this compensation can explain changes in tuning curves induced by neuron silencing across a variety of systems, including the primary visual cortex. We find that compensatory mechanisms can be implemented through the dynamics of networks with a tight balance of excitation and inhibition, without requiring synaptic plasticity. The limits of this compensatory mechanism are reached when excitation and inhibition become unbalanced, thereby demarcating a recovery boundary, where signal representation fails and where diseases may become symptomatic.

Entities:  

Keywords:  balanced networks; cercal system; efficient coding; neuron death; neuroscience; none; oculomotor; visual cortex

Mesh:

Year:  2016        PMID: 27935480      PMCID: PMC5283835          DOI: 10.7554/eLife.12454

Source DB:  PubMed          Journal:  Elife        ISSN: 2050-084X            Impact factor:   8.140


  60 in total

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Authors:  E Aksay; R Baker; H S Seung; D W Tank
Journal:  J Neurophysiol       Date:  2000-08       Impact factor: 2.714

3.  Recruitment order of cat abducens motoneurons and internuclear neurons.

Authors:  Angel M Pastor; David Gonzalez-Forero
Journal:  J Neurophysiol       Date:  2003-06-18       Impact factor: 2.714

4.  Efficient auditory coding.

Authors:  Evan C Smith; Michael S Lewicki
Journal:  Nature       Date:  2006-02-23       Impact factor: 49.962

5.  Independent component filters of natural images compared with simple cells in primary visual cortex.

Authors:  J H van Hateren; A van der Schaaf
Journal:  Proc Biol Sci       Date:  1998-03-07       Impact factor: 5.349

6.  Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex.

Authors:  D J Amit; N Brunel
Journal:  Cereb Cortex       Date:  1997 Apr-May       Impact factor: 5.357

7.  A common network architecture efficiently implements a variety of sparsity-based inference problems.

Authors:  Adam S Charles; Pierre Garrigues; Christopher J Rozell
Journal:  Neural Comput       Date:  2012-09-12       Impact factor: 2.026

8.  GABA-induced inactivation of functionally characterized sites in cat striate cortex: effects on orientation tuning and direction selectivity.

Authors:  J M Crook; Z F Kisvárday; U T Eysel
Journal:  Vis Neurosci       Date:  1997 Jan-Feb       Impact factor: 3.241

9.  GABA-induced inactivation of functionally characterized sites in cat visual cortex (area 18): effects on direction selectivity.

Authors:  J M Crook; Z F Kisvárday; U T Eysel
Journal:  J Neurophysiol       Date:  1996-05       Impact factor: 2.714

10.  Optogenetic perturbations reveal the dynamics of an oculomotor integrator.

Authors:  Pedro J Gonçalves; Aristides B Arrenberg; Bastian Hablitzel; Herwig Baier; Christian K Machens
Journal:  Front Neural Circuits       Date:  2014-02-28       Impact factor: 3.492

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  6 in total

1.  The geometry of robustness in spiking neural networks.

Authors:  Nuno Calaim; Florian A Dehmelt; Pedro J Gonçalves; Christian K Machens
Journal:  Elife       Date:  2022-05-30       Impact factor: 8.713

2.  Recurrent interactions in local cortical circuits.

Authors:  Simon Peron; Ravi Pancholi; Bettina Voelcker; Jason D Wittenbach; H Freyja Ólafsdóttir; Jeremy Freeman; Karel Svoboda
Journal:  Nature       Date:  2020-03-04       Impact factor: 69.504

3.  Fast-spiking GABA circuit dynamics in the auditory cortex predict recovery of sensory processing following peripheral nerve damage.

Authors:  Jennifer Resnik; Daniel B Polley
Journal:  Elife       Date:  2017-03-21       Impact factor: 8.140

4.  Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors.

Authors:  Julian Büchel; Dmitrii Zendrikov; Sergio Solinas; Giacomo Indiveri; Dylan R Muir
Journal:  Sci Rep       Date:  2021-12-03       Impact factor: 4.379

5.  Modularity and robustness of frontal cortical networks.

Authors:  Guang Chen; Byungwoo Kang; Jack Lindsey; Shaul Druckmann; Nuo Li
Journal:  Cell       Date:  2021-07-01       Impact factor: 66.850

6.  Learning to represent signals spike by spike.

Authors:  Wieland Brendel; Ralph Bourdoukan; Pietro Vertechi; Christian K Machens; Sophie Denève
Journal:  PLoS Comput Biol       Date:  2020-03-16       Impact factor: 4.475

  6 in total

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